SharpAI
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+# Legacy Applications (SharpAI-Hub CLI)
+
+> **Note:** These applications use the `sharpai-cli` Docker-based workflow.
+> For the modern experience, use [SharpAI Aegis](https://www.sharpai.org) — the desktop companion for DeepCamera.
+
+---
+
+## Application 1: Self-supervised Person Recognition (REID) for Intruder Detection
+
+SharpAI yolov7_reid is an open source python application that leverages AI technologies to detect intruders with traditional surveillance cameras. [Source code](https://github.com/SharpAI/DeepCamera/blob/master/src/yolov7_reid/src/detector_cpu.py)
+
+It leverages Yolov7 as person detector, FastReID for person feature extraction, Milvus the local vector database for self-supervised learning to identify unseen persons, Labelstudio to host images locally and for further usage such as labeling data and training your own classifier. It also integrates with Home-Assistant to empower smart home with AI technology.
+
+In simple terms, yolov7_reid is a person detector.
+
+<details>
+  <summary>Machine learning technologies</summary>
+
+  - Yolov7 Tiny, pretrained from COCO dataset
+  - FastReID ResNet50
+  - Vector Database Milvus for self-supervised learning
+</details>
+
+<details>
+  <summary>Supported Devices</summary>
+
+  - Nvidia Jetson
+    - [Nano (ReComputer j1010)](https://www.seeedstudio.com/Jetson-10-1-H0-p-5335.html)
+    - Xavier AGX
+  - Single Board Computer (SBC)
+    - Raspberry Pi 4GB
+    - Raspberry Pi 8GB
+  - Intel X64
+    - MacOS
+    - Windows
+    - Ubuntu
+  - MCU Camera
+    - ESP32 CAM
+    - ESP32-S3-Eye
+  - Tested Cameras/CCTV/NVR
+    - RTSP Camera (Lorex/Amrest/DoorBell)
+    - Blink Camera
+    - IMOU Camera
+    - Google Nest (Indoor/Outdoor)
+</details>
+
+<img src="../screenshots/reid_self_supervised.gif" width="960" height="480" />
+
+### Installation Guide
+
+```bash
+pip3 install sharpai-hub
+sharpai-cli yolov7_reid start
+```
+
+<details>
+   <summary><h4>Prerequisites</h4></summary>
+
+1. Docker (Latest version)
+2. Python (v3.6 to v3.10 will work fine)
+</details>
+
+<details>
+  <summary><h4>Step-by-step guide</h4></summary>
+
+```NOTE: Before executing any of commands mentioned below please start Docker.```
+```This guide is to install the sharpai and run the yolov7_reid service but can also be used to start other services.```
+
+1) Install SharpAI-Hub by running the following command in a Command Prompt and Terminal. Remember this as Command Prompt 1. This will be needed in further steps:
+	```
+	pip3 install sharpai-hub
+	```
+2) Now run the following command:
+	```
+	sharpai-cli yolov7_reid start
+	```
+**NOTE: If in a Windows system after running command mentioned in Step 2 if you get error:**
+`'sharpai-cli' is not recognized as an internal or external command, operable program or batch file.`
+Then it means environment variable is not set for Python on your system. More on this at the end of page in FAQ section.
+
+3) If you are using Windows and get error in step 2 you can also use following command line to start yolov7_reid
+
+```
+python3 -m sharpai_hub.cli yolov7_reid start
+```
+OR
+
+```
+python -m sharpai_hub.cli yolov7_reid start
+```
+
+4) Go to directory ```C:\Users``` and open the folder with name of current user. Here look for a folder  ```.sharpai``` . In ```.sharpai``` folder you will see a folder ```yolov7_reid```. Open it and start a new Command Prompt here. Remember this as ```Command Prompt 2```
+
+5) In Command Prompt 2 run the below command:
+
+```
+docker compose up
+```
+
+**NOTE: DO NOT TERMINATE THIS COMMAND.** Let it complete. After running the above command it will take roughly 15-20 minutes or even more time to complete depending upon your system specifications and internet speed. After 5-10 minutes of running the command in the images tab of Docker will images will start to appear. If the command ran successful then there must be seven images in images tab plus one container named as `yolov7_reid` in the container tab.
+
+6) Go to folder ```yolov7_reid``` mentioned in step 4. In this folder there will be file ```.env```. Delete it. Now close the Command Prompt 1. Open and new Command prompt and run the following command again. We will call this as Command Prompt 3.
+
+```
+sharpai-cli yolov7_reid start
+```
+OR
+
+```
+python3 -m sharpai_hub.cli yolov7_reid start
+```
+OR
+
+```
+python -m sharpai_hub.cli yolov7_reid start
+```
+
+7) Running command in Step 6 will open a Signup/Signin page in the browser and in Command Prompt it will ask for the Labelstudio Token. After Signing up in you will be taken to your account. At the top right corner you will see a small circle with your account initials. Click on it and after that click on `Account Setting`. Here at the right side of page you will see an Access token. Copy the token and paste it carefully in the command prompt 3.
+
+8) Add Camera to Home-Assistant, you can use "Generic Camera" to add camera with RTSP url
+
+9) In this step, we will obtain the camera entity ID of your cameras. After adding your camera to `home-Assistant`, go to the `Overview` tab. Here all your cameras will be listed. Click on the video stream of a camera, after which a small popup will open. At the top right of the popup, click the gear icon to open the settings page. A new popup will open with a few editable properties. Here look for Entity ID, which is in the format `camera.IP_ADDRESS_OF_CAMERA`, copy/note this entity ID (these entity ids will be required later). If you have multiple cameras, we will need each cameras Entity ID. Note all these camera entity IDs.
+
+10) Run following two commands to open and edit the `configuration.yaml` of Home-Assistant:
+
+```
+docker exec -ti home-assistant /bin/bash
+```
+
+```
+vi configuration.yaml
+```
+
+**NOTE FOR WINDOWS SYSTEM USERS:** These commands won't work with Windows systems. For Windows system, please open Docker (the instance of Docker, which is already running from the start) and in the container tab, open the `yolov7_reid`. Here look for the `home-assistant` container. Hover your mouse cursor on the `home-assistant` container, and a few options will appear. Click on `cli`. An inbuilt console will start on the same page. If the typing cursor keeps blinking and nothing shows up on the inbuilt console, then click on `Open in External Terminal`, which is just above the blinking cursor. After clicking it, a new command prompt will open. To check everything is working as expected, run the command `ls` and see if the commands list the files and folders in the config folder.
+
+Now run a command `vi configuration.yaml`. This command will open your configuration file of the `home-assistant` in the Vi editor. Vi Editor is a bit tricky if you are unfamiliar with using it. You will now have to enter into Insert mode to add the integration code mentioned in Step 9 to the configuration file. Press the `I` key to enter Insert mode and go end of the file using the down arrow key. Next, press the right mouse (while the mouse cursor is inside the command prompt window) while in the command prompt. This will paste the integration code that you had copied earlier. After making changes to the config file, press the escape key, type the following `:wq` (yes with colon) and press enter key. You will be back taken to `/config #`. This command `:wq` means you want to write changes to the config file and quit. You can now close the command prompt.
+
+11) Add the below code to the end of `configuration.yaml` file.
+
+**Here, replace `camera.<camera_entity_id>` with the camera entity ID we obtained in Step 9. If you have multiple cameras then keep adding the `entity_id` under `images_processing`.**
+
+```yaml
+stream:
+  ll_hls: true
+  part_duration: 0.75
+  segment_duration: 6
+
+image_processing:
+  - platform: sharpai
+    source:
+      - entity_id: camera.<camera_entity_id>
+    scan_interval: 1
+```
+
+If you have multiple cameras then after changing the 'entity_id' the code will become similar to this:
+
+```yaml
+stream:
+  ll_hls: true
+  part_duration: 0.75
+  segment_duration: 6
+
+image_processing:
+  - platform: sharpai
+    source:
+      - entity_id: camera.192_168_29_44
+      - entity_id: camera.192_168_29_45
+      - entity_id: camera.192_168_29_46
+      - entity_id: camera.192_168_29_47
+    scan_interval: 1
+```
+
+12) At `home-assistant` homepage `http://localhost:8123` select `Developer Tools`. Look for and click `Check Configuration` under `Configuration Validation`. If everything went well then it must show "Configuration Valid". Click `Restart`. Now go to the `container` tab of docker, click three vertical dots under `Actions` and press restart. Open the `Overview` tab of `home-assistant`. If you see `Image Processing` beside your cameras and below it `Sharp IP_ADDRESS_OF_YOUR_CAMERA`, then congrats. Everything is working as expected.
+
+```NOTE: Till further steps are added you can use demo video in the beginning tutorial for further help.```
+
+</details>
+
+<details>
+  <summary><h4>Important Links</h4></summary>
+
+The yolov7 detector is running in docker, you can access the docker desktop with http://localhost:8000
+Home-Assistant is hosted at http://localhost:8123
+Labelstudio is hosted at http://localhost:8080
+</details>
+
+---
+
+## Application 2: Facial Recognition Based Intruder Detection (Local)
+
+We received feedback from the community — local deployment is needed. With local DeepCamera deployment, all information/images will be saved locally.
+
+```bash
+sharpai-cli local_deepcamera start
+```
+
+---
+
+## Application 3: DeepCamera Facial Recognition (Cloud — Free)
+
+- Register account on [SharpAI website](http://dp.sharpai.org:3000)
+- Login on device: `sharpai-cli login`
+- Register device: `sharpai-cli device register`
+- Start DeepCamera: `sharpai-cli deepcamera start`
+
+---
+
+## Application 4: Laptop Screen Monitor (Child Safety)
+
+SharpAI Screen monitor captures screen, extracts image features (embeddings) with AI model, saves unseen features into AI vector database [Milvus](https://milvus.io/), and stores raw images to [Labelstudio](https://labelstud.io) for labeling and model training. All information/images are saved locally.
+
+```bash
+sharpai-cli screen_monitor start
+```
+
+- Access streaming screen: http://localhost:8000
+- Access Labelstudio: http://localhost:8080
+
+---
+
+## Application 5: Person Detector
+
+```bash
+sharpai-cli yolov7_person_detector start
+```
+
+---
+
+## SharpAI-Hub Application Catalog
+
+SharpAI community is continually working on bringing state-of-the-art computer vision applications to your device.
+
+```bash
+sharpai-cli <application name> start
+```
+
+| Application | SharpAI CLI Name | OS/Device |
+|---|---|---|
+| Intruder detection with Person shape | yolov7_reid | Jetson Nano/AGX/Windows/Linux/MacOS |
+| Person Detector | yolov7_person_detector | Jetson Nano/AGX/Windows/Linux/MacOS |
+| [Laptop Screen Monitor](https://github.com/SharpAI/laptop_monitor) | screen_monitor | Windows/Linux/MacOS |
+| [Facial Recognition Intruder Detection](how_to_run_intruder_detection.md) | deepcamera | Jetson Nano/Windows/Linux/MacOS |
+| [Local Facial Recognition Intruder Detection](how_to_run_local_intruder_detection.md) | local_deepcamera | Windows/Linux/MacOS |
+| [Parking Lot Monitor](Yolo_Parking.md) | yoloparking | Jetson AGX |
+| [Fall Detection](FallDetection_with_shinobi.md) | falldetection | Jetson AGX |
+
+---
+
+## Tested Devices
+
+### Edge AI Devices / Workstation
+- [Jetson Nano (ReComputer j1010)](https://www.seeedstudio.com/Jetson-10-1-H0-p-5335.html)
+- Jetson Xavier AGX
+- MacOS 12.4
+- Windows 11
+- Ubuntu 20.04
+
+### Tested Cameras
+- DaHua / Lorex / AMCREST: URL Path: `/cam/realmonitor?channel=1&subtype=0` Port: `554`
+- IP Camera Lite on iOS: URL Path: `/live` Port: `8554`
+- Nest Camera indoor/outdoor by Home-Assistant integration
+
+---
+
+## ❓ FAQ
+
+### Installation & Setup
+- [How to install Python3](https://www.python.org/downloads)
+- [How to install pip3](https://pip.pypa.io/en/stable/installation)
+- [How to configure RTSP on GUI](https://github.com/SharpAI/DeepCamera/blob/master/docs/shinobi.md)
+- [Camera streaming URL formats](https://shinobi.video)
+
+### Jetson Nano Docker-compose
+```bash
+sudo apt-get install -y libhdf5-dev python3 python3-pip
+pip3 install -U pip
+sudo pip3 install docker-compose==1.27.4
+```
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+# Aegis Skill Platform Parameters
+
+Aegis automatically injects these environment variables to every skill process. Skills should **not** ask users to configure these — they are provided by the platform.
+
+## Platform Parameters (auto-injected)
+
+| Env Var | Type | Description |
+|---------|------|-------------|
+| `AEGIS_GATEWAY_URL` | `string` | LLM gateway endpoint (e.g. `http://localhost:5407`). Proxies to whatever LLM provider the user has configured (OpenAI, Anthropic, local). Skills should use this for all LLM calls — it handles auth, routing, and model selection. |
+| `AEGIS_VLM_URL` | `string` | Local VLM (Vision Language Model) server endpoint (e.g. `http://localhost:5405`). Available when the user has a local VLM running. |
+| `AEGIS_SKILL_ID` | `string` | The skill's unique identifier (e.g. `home-security-benchmark`). |
+| `AEGIS_SKILL_PARAMS` | `JSON string` | User-configured parameters from `config.yaml` (see below). |
+| `AEGIS_PORTS` | `JSON string` | All Aegis service ports as JSON. Use the URL vars above instead of parsing this directly. |
+
+## User Parameters (from config.yaml)
+
+Skills can define user-configurable parameters in a `config.yaml` file alongside `SKILL.md`. Aegis parses this at install time and renders a config panel in the UI. User values are passed as JSON via `AEGIS_SKILL_PARAMS`.
+
+### config.yaml Format
+
+```yaml
+params:
+  - key: mode
+    label: Test Mode
+    type: select
+    options: [option1, option2, option3]
+    default: option1
+    description: "Human-readable description shown in the config panel"
+
+  - key: verbose
+    label: Verbose Output
+    type: boolean
+    default: false
+    description: "Enable detailed logging"
+
+  - key: threshold
+    label: Confidence Threshold
+    type: number
+    default: 0.7
+    description: "Minimum confidence score (0.0–1.0)"
+
+  - key: apiEndpoint
+    label: Custom API Endpoint
+    type: string
+    default: ""
+    description: "Optional override for external API"
+```
+
+Supported types: `string`, `boolean`, `select`, `number`
+
+### Reading config.yaml in Your Skill
+
+```javascript
+// Node.js — parse AEGIS_SKILL_PARAMS
+let skillParams = {};
+try { skillParams = JSON.parse(process.env.AEGIS_SKILL_PARAMS || '{}'); } catch {}
+
+const mode = skillParams.mode || 'default';
+const verbose = skillParams.verbose || false;
+```
+
+```python
+# Python — parse AEGIS_SKILL_PARAMS
+import os, json
+
+skill_params = json.loads(os.environ.get('AEGIS_SKILL_PARAMS', '{}'))
+mode = skill_params.get('mode', 'default')
+verbose = skill_params.get('verbose', False)
+```
+
+### Precedence
+
+```
+CLI flags > AEGIS_SKILL_PARAMS > Platform env vars > Defaults
+```
+
+When a skill supports both CLI arguments and `AEGIS_SKILL_PARAMS`, CLI flags should take priority. Platform-injected env vars (like `AEGIS_GATEWAY_URL`) are always available regardless of `config.yaml`.
+
+## Gateway as Proxy
+
+The gateway (`AEGIS_GATEWAY_URL`) is an OpenAI-compatible proxy. Skills call it like any OpenAI endpoint — the gateway handles:
+
+- **API key management** — user configures keys in Aegis settings
+- **Provider routing** — OpenAI, Anthropic, local models
+- **Model selection** — user picks model in Aegis UI
+
+Skills should **not** need raw API keys. If a skill needs direct provider access in the future, Aegis will expose additional env vars (`AEGIS_LLM_API_KEY`, `AEGIS_LLM_PROVIDER`, etc.) — but this is not yet implemented.
+
+### Example: Calling the Gateway
+
+```javascript
+const gatewayUrl = process.env.AEGIS_GATEWAY_URL || 'http://localhost:5407';
+
+const response = await fetch(`${gatewayUrl}/v1/chat/completions`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+        messages: [{ role: 'user', content: 'Hello' }],
+        stream: false,
+    }),
+});
+```
+
+No API key header needed — the gateway injects it.